Matching color and appearance of target coatings based on image entropy

ABSTRACT

Processor implemented systems and methods for matching color and appearance of a target coating are provided herein. A system includes a storage device for storing instructions, and one or more data processors. The data processor(s) are configured to execute instructions to receive a target image of a target coating. The data processor(s) are also configured to apply a feature extraction analysis process that divides the target image into a plurality of target pixels for image analysis.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a U.S. National-Stage entry under 35 U.S.C. § 371based on International Application No. PCT/US2018/065238, filed Dec. 12,2018, which was published under PCT Article 21(2) and is a continuationin part of U.S. patent application Ser. No. 15/833,597, filed Dec. 6,2017, which are herein incorporated by reference.

TECHNICAL FIELD

The technical field is directed to coatings technology and moreparticularly to systems and methods for matching color and appearance oftarget coatings.

BACKGROUND

Visualization and selection of coatings having a desired color andappearance play an important role in many applications. For example,paint suppliers must provide thousands of coatings to cover the range ofglobal OEM manufacturers' coatings for all current and recent modelvehicles. Providing this large number of different coatings as factorypackage products adds complexity to paint manufacture and increasesinventory costs. Consequently, paint suppliers provide a mixing machinesystem including typically 50 to 100 components (e.g., single pigmenttints, binders, solvents, additives) with coating formulas for thecomponents that match the range of coatings of vehicles. The mixingmachine may reside at a repair facility (i.e., body shop) or a paintdistributor and allows a user to obtain the coating having the desiredcolor and appearance by dispensing the components in amountscorresponding to the coating formula. The coating formulas are typicallymaintained in a database and are distributed to customers via computersoftware by download or direct connection to internet databases. Each ofthe coating formulas typically relate to one or more alternate coatingformulas to account for variations in coatings due to variations invehicle production.

Identification of the coating formula most similar to a target coatingis complicated by this variation. For example, a particular coatingmight appear on three vehicle models, produced in two assembly plantswith various application equipment, using paint from two OEM paintsuppliers, and over a lifetime of five model years. These sources ofvariation result in significant coating variation over the population ofvehicles with that particular coating. The alternate coating formulasprovided by the paint supplier are matched to subsets of the colorpopulation so that a close match is available for any vehicle that needsrepair. Each of the alternate coating formulas can be represented by acolor chip in the fan deck which enables the user to select the bestmatching formula by visual comparison to the vehicle.

Identifying the coating formula most similar to the target coating for arepair is typically accomplished through either the use aspectrophotometer or a fandeck. Spectrophotometers measure one or morecolor and appearance attributes of the target coating to be repaired.This color and appearance data is then compared with the correspondingdata from potential candidate formulas contained in a database. Thecandidate formula whose color and appearance attributes best match thoseof the target coating to be repaired is then selected as the coatingformula most similar to the target coating. However, spectrophotometersare expensive and not readily available in economy markets.

Alternatively, fandecks include a plurality of sample coating layers onpages or patches within the fandeck. The sample coating layers of thefandeck are then visually compared to the target coating being repaired.The formula associated with the sample coating layer best matching thecolor and appearance attributes of the target coating to be repaired isthen selected as the coating formula most similar to the target coating.However, fandecks are cumbersome to use and difficult to maintain due tothe vast number of sample coating layers necessary to account for allcoatings on vehicles on the road today.

As such, it is desirable to provide a system and a method for matchingcolor and appearance of a target coating. In addition, other desirablefeatures and characteristics will become apparent from the subsequentsummary and detailed description, and the appended claims, taken inconjunction with the accompanying drawings and this background.

SUMMARY

Various non-limiting embodiments of a system for matching color andappearance of a target coating, and various non-limiting embodiments ofmethods for the same, are disclosed herein. In one non-limitingembodiment, the system includes, but is not limited to, a storage devicefor storing instructions, and one or more data processors. The one ormore data processors are configured to execute instructions to receive atarget image of a target coating. The data processor(s) are alsoconfigured to apply a feature extraction analysis process to determine atarget image feature, and to determine a calculated match sample imagewith the target image feature. The feature extraction analysis processcomprises analyzing a plurality of target pixels within the targetimage.

The system described above wherein the one or more data processors areconfigured to execute instructions to compare a sample image feature ofa sample image to the target image feature.

The system described above wherein the one or more data processors areconfigured to apply the feature extraction analysis process, wherein thefeature extraction analysis process determines one or more of: L*a*b*color coordinates of the individual target pixels of the target image;average L*a*b* color coordinates from the individual target pixels for atotal image of the target image; sparkle area of a black and white imageof the target image; sparkle intensity of the black and white image ofthe target image; sparkle grade of the black and white image of thetarget image; sparkle color determination of the target image; sparkleclustering of the target image; sparkle color differences within thetarget image; sparkle persistence of the target image, where sparklepersistence is a measure of a sparkle as a function of one or moreillumination changes during capture of the target image; colorconstancy, at a target pixel level, with one or more illuminationchanges during capture of the target image; wavelet coefficients of thetarget image at the target pixel level; Fourier coefficients of thetarget image at the target pixel level; average color of a local areawithin the target image, where the local area may be one or more targetpixels, but where the local area is less than a total area of the targetimage; pixel count in discrete L*a*b* ranges of the target image, wherethe L*a*b* range may be fixed or may be data driven such that the rangevaries; maximally populated coordinates of cubic bins at the targetpixel level of the target image, where the cubic bins are based on a 3dimensional coordinate mapping using L*a*b* or RGB values; overall imagecolor entropy of the target image; image entropy of one or more of theL*a*b* planes as a function of the 3^(rd) dimension of the target image;image entropy of one or more of an RGB plane as a function of the 3^(rd)dimension of the target image; local target pixel variation metrics ofthe target image; coarseness of the target image; vectors of highvariance of the target image, where the vectors of high variance areestablished using principle component analysis; and vectors of highkurtosis of the target image, where the vectors of high kurtosis areestablished using independent component analysis.

The system described above wherein the one or more data processors areconfigured to execute instructions to retrieve a mathematical model todetermine the calculated match sample image.

The system described above wherein the mathematical model is amachine-learning model.

The system described above wherein the one or more data processors areconfigured to determine a coating formula that corresponds to thecalculated match sample image.

The system described above wherein the one or more data processors areconfigured to determine a plurality of the coating formulas thatcorrespond to the calculated match sample image, wherein the pluralityof coating formulas comprise different grades of coatings.

The system described above wherein the one or more data processors areconfigured to reference a sample database to determine the coatingformula that corresponds to the calculated match sample image.

The system described above wherein the one or more data processors areconfigured to reference a sample database to determine the calculatedmatch sample image.

The system described above wherein the one or more data processors areconfigured to receive the target image data of the target coating,wherein the target image data correlates to a plurality of images of thetarget coating with varying angles of light relative to an imagingdevice.

The system described above wherein the one or more data processors areconfigured to receive the target image data of the target coating,wherein the target image data correlates to a plurality of images of thetarget coating with varying magnification.

The system described above wherein the target coating is a metalliccoating, a pearlescent coating, or a combination of thereof.

The system described above also including an imaging device, wherein theimaging device is configured to generate the target image data of thetarget coating.

The system described above wherein the one or more data processors arefurther configured to retrieve a sample image from a sample database;extract a sample image feature from the sample image utilizing thefeature extraction analysis process; and generate the one or morepre-specified matching criteria based on the sample image feature.

The system described above wherein the one or more data processors areconfigured to determine the calculated match sample image with about thesame color as that of the target image.

In another non-limiting embodiment, the method includes, but is notlimited to, obtaining a target image of a target coating by one or moredata processors, where the target coating is an effect pigment-basedcoating. The target image is divided into a plurality of target pixelswhich are analyzed, and a calculated match sample image is determined.

The method described above wherein applying the feature extractionanalysis process comprises applying the feature extraction analysisprocess wherein the feature extraction analysis process comprises one ormore of: determining L*a*b* color coordinates of the individual targetpixels of the target image; determining average L*a*b* color coordinatesfrom the individual target pixels for a total image of the target image;determining sparkle area of a black and white image of the target image;determining sparkle intensity of the black and white image of the targetimage; determining sparkle grade of the black and white image of thetarget image; determining sparkle color of the target image; determiningsparkle clustering of the target image; determining sparkle colordifferences within the target image; determining sparkle persistence ofthe target image, where sparkle persistence is a measure of a sparkle asa function of one or more illumination changes during capture of thetarget image; determining color constancy, at a target pixel level, withone or more illumination changes during capture of the target image;determining wavelet coefficients of the target image at the target pixellevel; determining Fourier coefficients of the target image at thetarget pixel level; determining average color of a local area within thetarget image, where the local area may be one or more target pixels, butwhere the local area is less than a total area of the target image;determining pixel count in discrete L*a*b* ranges of the target image,where the L*a*b* range may be fixed or may be data driven such that therange varies; determining maximally populated coordinates of cubic binsat the target pixel level of the target image, where the cubic bins arebased on a 3 dimensional coordinate mapping using L*a*b* or RGB values;determining overall image color entropy of the target image; determiningimage entropy of one or more of the L*a*b* planes as a function of the3^(rd) dimension of the target image; determining image entropy of oneor more of an RGB planes as a function of the 3^(rd) dimension of thetarget image; determining local target pixel variation metrics of thetarget image; determining coarseness of the target image; determiningvectors of high variance of the target image, where the vectors of highvariance are established using principle component analysis; anddetermining vectors of high kurtosis of the target image, where thevectors of high kurtosis are established using independent componentanalysis.

The method described above wherein determining the calculated matchsample image comprises determining a plurality of calculated matchsample images, wherein the plurality of calculated match sample imagescomprise different grades of a coating.

The method described above wherein determining the calculated matchsample image comprises comparing a sample image feature of a sampleimage to the target image feature.

The method described above also including determining a coating formulathat corresponds to the calculated match sample image, wherein thecoating formula comprises an effect additive

BRIEF DESCRIPTION OF THE DRAWINGS

Other advantages of the disclosed subject matter will be readilyappreciated, as the same becomes better understood by reference to thefollowing detailed description when considered in connection with theaccompanying drawings wherein:

FIG. 1 is a perspective view illustrating a non-limiting embodiment of asystem for matching color and appearance of a target coating;

FIG. 2 is a block diagram illustrating a non-limiting embodiment of thesystem of FIG. 1 ;

FIG. 3A is an image illustrating a non-limiting embodiment of the targetcoating of FIG. 1 ;

FIG. 3B is a graphical representation of RGB values illustrating anon-limiting embodiment of the target coating of FIG. 2A;

FIG. 4A is an image illustrating a non-limiting embodiment of a firstsample image of the system of FIG. 1 ;

FIG. 4B is a graphical representation of RGB values illustrating anon-limiting embodiment of the first sample image of FIG. 4A;

FIG. 5A is an image illustrating a non-limiting embodiment of a secondsample image of the system of FIG. 1 ;

FIG. 5B is a graphical representation of RGB values illustrating anon-limiting embodiment of the second sample image of FIG. 5A;

FIG. 6 is a perspective view illustrating a non-limiting embodiment ofan electronic imaging device of the system of FIG. 1 ;

FIG. 7 is another perspective view illustrating a non-limitingembodiment of an electronic imaging device of the system of FIG. 1 ;

FIG. 8 is a flow chart illustrating a non-limiting embodiment of thesystem of FIG. 1 ;

FIG. 9 is a flow chart illustrating a non-limiting embodiment of themethod of FIG. 8 ;

FIG. 10 is a flow chart illustrating another non-limiting embodiment ofthe method of FIG. 8 ;

FIG. 11 is a schematic diagram illustrating formation of a crosssectional portion of a substrate and a coating;

FIGS. 12 and 13 are schematic diagrams illustrating capturing an imageof a coating;

FIGS. 14 and 15 are flow charts illustrating an embodiment of a systemand method;

FIG. 16 is a hypothetical diagram illustrating one possible portion of atechnique to determine a target and/or sample feature;

FIGS. 17-19 are flow charts illustrating embodiments of methods; and

FIG. 20 is a schematic diagram illustrating application of a repaircoating to a substrate.

DETAILED DESCRIPTION

The following detailed description includes examples and is not intendedto limit the invention or the application and uses of the invention.Furthermore, there is no intention to be bound by any theory presentedin the preceding background or the following detailed description. Itshould be understood that throughout the drawings, correspondingreference numerals indicate like or corresponding parts and features.

The features and advantages identified in the present disclosure will bemore readily understood, by those of ordinary skill in the art, fromreading the following detailed description. It is to be appreciated thatcertain features, which are, for clarity, described above and below inthe context of separate embodiments, may also be provided in combinationin a single embodiment. Conversely, various features that are, forbrevity, described in the context of a single embodiment, may also beprovided separately or in any sub-combination. In addition, referencesin the singular may also include the plural (for example, “a” and “an”may refer to one, or one or more) unless the context specifically statesotherwise.

The use of numerical values in the various ranges specified in thisdisclosure, unless expressly indicated otherwise, are stated asapproximations as though the minimum and maximum values within thestated ranges were both proceeded by the word “about.” In this manner,slight variations above and below the stated ranges can be used toachieve substantially the same results as values within the ranges.Also, the disclosure of these ranges is intended as a continuous rangeincluding every value between the minimum and maximum values.

Techniques and technologies may be described herein in terms offunctional and/or logical block components, and with reference tosymbolic representations of operations, processing tasks, and functionsthat may be performed by various computing components or devices. Itshould be appreciated that the various block components shown in thefigures may be realized by any number of hardware, software, and/orfirmware components configured to perform the specified functions. Forexample, an embodiment of a system or a component may employ variousintegrated circuit components, e.g., memory elements, digital signalprocessing elements, logic elements, look-up tables, or the like, whichmay carry out a variety of functions under the control of one or moremicroprocessors or other control devices.

The following description may refer to elements or nodes or featuresbeing “coupled” together. As used herein, unless expressly statedotherwise, “coupled” means that one element/node/feature is directly orindirectly joined to (or directly or indirectly communicates with)another element/node/feature, and not necessarily mechanically. Thus,although the drawings may depict one example of an arrangement ofelements, additional intervening elements, devices, features, orcomponents may be present in an embodiment of the depicted subjectmatter. In addition, certain terminology may also be used in thefollowing description for the purpose of reference only, and thus arenot intended to be limiting.

Techniques and technologies may be described herein in terms offunctional and/or logical block components and with reference tosymbolic representations of operations, processing tasks, and functionsthat may be performed by various computing components or devices. Suchoperations, tasks, and functions are sometimes referred to as beingcomputer-executed, computerized, software-implemented, orcomputer-implemented. In practice, one or more processor devices cancarry out the described operations, tasks, and functions by manipulatingelectrical signals representing data bits at memory locations in thesystem memory, as well as other processing of signals. The memorylocations where data bits are maintained are physical locations thathave particular electrical, magnetic, optical, or organic propertiescorresponding to the data bits. It should be appreciated that thevarious block components shown in the figures may be realized by anynumber of hardware, software, and/or firmware components configured toperform the specified functions. For example, an embodiment of a systemor a component may employ various integrated circuit components, e.g.,memory elements, digital signal processing elements, logic elements,look-up tables, or the like, which may carry out a variety of functionsunder the control of one or more microprocessors or other controldevices.

For the sake of brevity, conventional techniques related to graphics andimage processing, touchscreen displays, and other functional aspects ofcertain systems and subsystems (and the individual operating componentsthereof) may not be described in detail herein. Furthermore, theconnecting lines shown in the various figures contained herein areintended to represent an example of functional relationships and/orphysical couplings between the various elements. It should be noted thatmany alternative or additional functional relationships or physicalconnections may be present in an embodiment of the subject matter.

As used herein, the term “module” refers to any hardware, software,firmware, electronic control component, processing logic, and/orprocessor device, individually or in any combination, including withoutlimitation: application specific integrated circuit (ASIC), anelectronic circuit, a processor (shared, dedicated, or group) and memorythat executes one or more software or firmware programs, a combinationallogic circuit, and/or other suitable components that provide thedescribed functionality.

As used herein, the term “pigment” or “pigments” refers to a colorant orcolorants that produce color or colors. A pigment can be from natural orsynthetic sources and can be made of organic or inorganic constituents.Pigments can also include metallic particles or flakes with specific ormixed shapes and dimensions. A pigment is usually not soluble in acoating composition.

The term “effect pigment” or “effect pigments” refers to pigments thatproduce special effects in a coating. Examples of effect pigmentsinclude, but are not limited to, light scattering pigments, lightinterference pigments, and light reflecting pigments. Metallic flakes,such as aluminum flakes, and pearlescent pigments, such as mica-basedpigments, are examples of effect pigments.

The term “appearance” can include: (1) the aspect of visual experienceby which a coating is viewed or recognized; and (2) perception in whichthe spectral and geometric aspects of a coating is integrated with itsilluminating and viewing environment. In general, appearance includestexture, coarseness, sparkle, or other visual effects of a coating,especially when viewed from varying viewing angles and/or with varyingillumination conditions. Appearance characteristics or appearance datacan include, but not limited to, descriptions or measurement data ontexture, metallic effect, pearlescent effect, gloss, distinctness ofimage, flake appearances and sizes such as texture, coarseness, sparkle,glint and glitter as well as the enhancement of depth perception in thecoatings imparted by the flakes, especially produced by metallic flakes,such as aluminum flakes. Appearance characteristics can be obtained byvisual inspection or by using an appearance measurement device.

The term “color data” or “color characteristics” of a coating cancomprise measured color data including spectral reflectance values,X,Y,Z values, L*, a*, b* values, L*,a*,b* values, L,C,h values, or acombination thereof. Color data can further comprise a color code of avehicle, a color name or description, or a combination thereof. Colordata can even further comprise visual aspects of color of the coating,chroma, hue, lightness or darkness. The color data can be obtained byvisual inspection, or by using a color measurement device such as acolorimeter, a spectrophotometer, or a goniospectrophotometer. Inparticular, spectrophotometers obtain color data by determining thewavelength of light reflected by a coating layer. The color data canalso comprise descriptive data, such as a name of a color, a color codeof a vehicle; a binary, textural or encrypted data file containingdescriptive data for one or more colors; a measurement data file, suchas those generated by a color measuring device; or an export/import datafile generated by a computing device or a color measuring device. Colordata can also be generated by an appearance measuring device or acolor-appearance dual measuring device.

The term “coating” or “coating composition” can include any coatingcompositions known to those skilled in the art and can include atwo-pack coating composition, also known as “2K coating composition”; aone-pack or 1K coating composition; a coating composition having acrosslinkable component and a crosslinking component; a radiationcurable coating composition, such as a UV curable coating composition oran E-beam curable coating composition; a mono-cure coating composition;a dual-cure coating composition; a lacquer coating composition; awaterborne coating composition or aqueous coating composition; a solventborne coating composition; or any other coating compositions known tothose skilled in the art. The coating composition can be formulated as aprimer, a basecoat, or a color coat composition by incorporating desiredpigments or effect pigments. The coating composition can also beformulated as a clearcoat composition.

The term “vehicle”, “automotive”, “automobile” or “automotive vehicle”can include an automobile, such as car, bus, truck, semi-truck, pickuptruck, SUV (Sports Utility Vehicle); tractor; motorcycle; trailer; ATV(all-terrain vehicle); heavy duty mover, such as, bulldozer, mobilecrane and earth mover; airplanes; boats; ships; and other modes oftransport.

The term “formula,” “matching formula,” or “matching formulation” for acoating composition refers to a collection of information orinstruction, based upon that, the coating composition can be prepared.In one example, a matching formula includes a list of names andquantities of pigments, effect pigments, and other components of acoating composition. In another example, a matching formula includesinstructions on how to mix multiple components of a coating composition.

A processor-implemented system 10 for matching color and appearance of atarget coating 12 is provided herein with reference to FIG. 1 . Thetarget coating 12 may be on a substrate 14. The substrate 14 may be avehicle or parts of a vehicle. The substrate 14 may also be any coatedarticle including the target coating 12. The target coating 12 mayinclude a color coat layer, a clearcoat layer, or a combination of acolor coat layer and a clearcoat layer. The color coat layer may beformed from a color coat composition. The clearcoat layer may be formedfrom a clearcoat coating composition. The target coating 12 may beformed from one or more solvent borne coating compositions, one or morewaterborne coating compositions, one or more two-pack coatingcompositions or one or more one-pack coating compositions. The targetcoating 12 may also be formed from one or more coating compositions eachhaving a crosslinkable component and a crosslinking component, one ormore radiation curable coating compositions, or one or more lacquercoating compositions.

With reference to FIG. 2 and continued reference to FIG. 1 , the system10 includes an electronic imaging device 16 configured to generatetarget image data 18 of the target coating 12. The electronic imagingdevice 16 may be a device that can capture images under a wide range ofelectromagnetic wavelengths including visible or invisible wavelengths.The electronic imaging device 16 may be further defined as a mobiledevice. Examples of mobile devices include, but are not limited to, amobile phone (e.g., a smartphone), a mobile computer (e.g., a tablet ora laptop), a wearable device (e.g., smart watch or headset), or anyother type of device known in the art configured to receive the targetimage data 18. In one embodiment, the mobile device is a smartphone or atablet.

In embodiments, the electronic imaging device 16 includes a camera 20(see FIG. 7 ). The camera 20 may be configured to obtain the targetimage data 18. The camera 20 may be configured to capture images havingvisible wavelengths. The target image data 18 may be derived from atarget image 58 of the target coating 12, such as a still image or avideo. In certain embodiments, the target image data 18 is derived froma still image. In the embodiment shown in FIG. 1 , the electronicimaging device 16 is shown disposed in the proximity of and spaced fromthe target coating 12. However, it should be appreciated that theelectronic imaging device 16 of the embodiment is portable, such that itmay be moved to another coating (not shown). In other embodiments (notshown), the electronic imaging device 16 may be fixed at a location. Inyet other embodiments (not shown), the electronic imaging device 16 maybe attached to a robotic arm to be moved automatically. In furtherembodiments (not shown), the electronic imaging device 16 may beconfigured to measure characteristics of multiple surfacessimultaneously.

The system 10 further includes a storage device 22 for storinginstructions for performing the matching of color and appearance of thetarget coating 12. The storage device 22 may store instructions that canbe performed by one or more data processors 24. The instructions storedin the storage device 22 may include one or more separate programs, eachof which includes an ordered listing of executable instructions forimplementing logical functions. When the system 10 is in operation, theone or more data processors 24 are configured to execute theinstructions stored within the storage device 22, to communicate data toand from the storage device 22, and to generally control operations ofthe system 10 pursuant to the instructions. In certain embodiments, thestorage device 22 is associated with (or alternatively included within)the electronic imaging device 16, a server associated with the system10, a cloud-computing environment associated with the system 10, orcombinations thereof.

As introduced above, the system 10 further includes the one or more dataprocessors 24 configured to execute the instructions. The one or moredata processors 24 are configured to be communicatively coupled with theelectronic imaging device 16. The one or more data processors 24 can beany custom made or commercially available processor, a centralprocessing unit (CPU), an auxiliary processor among several processorsassociated with the electronic imaging device 16, a semiconductor basedmicroprocessor (in the form of a microchip or chip set), or generallyany device for executing instructions. The one or more data processors24 may be communicatively coupled with any component of the system 10through wired connections, wireless connections and/or devices, or acombination thereof. Examples of suitable wired connections includes,but are not limited to, hardware couplings, splitters, connectors,cables or wires. Examples of suitable wireless connections and devicesinclude, but not limited to, Wi-Fi device, Bluetooth device, wide areanetwork (WAN) wireless device, Wi-Max device, local area network (LAN)device, 3G broadband device, infrared communication device, optical datatransfer device, radio transmitter and optionally receiver, wirelessphone, wireless phone adaptor card, or any other devices that cantransmit signals in a wide range of electromagnetic wavelengthsincluding radio frequency, microwave frequency, visible or invisiblewavelengths.

With reference to FIGS. 3A and 3B, the one or more data processors 24are configured to execute the instructions to receive, by the one ormore data processors 24, target image data 18 of the target coating 12.As described above, the target image data 18 is generated by theelectronic imaging device 16. The target image data 18 may define RGBvalues, L*a*b* values, or a combination thereof, representative of thetarget coating 12. In certain embodiments, the target image data 18defines the RGB values representative of the target coating 12. The oneor more data processors 24 may be further configured to execute theinstructions to transform the RGB values of the target image data 18 toL*a*b* values representative of the target coating 12.

The target image data 18 includes target image features 26. The targetimage features 26 may include color and appearance characteristics ofthe target coating 12, representations of the target image data 18, or acombination thereof. In certain embodiments, the target image features26 may include representations based on image entropy.

The one or more data processors 24 are configured to execute theinstructions to retrieve, by the one or more data processors 24, one ormore feature extraction analysis processes 28′ that extract the targetimage features 26 from the target image data 18. In embodiments, the oneor more feature extraction analysis processes 28′ are configured toidentify the representation based on image entropy for extracting thetarget image features 26 from the target image data 18. To this end, theone or more data processors 24 may be configured to execute theinstructions to identify the representation based on image entropy forextracting the target image features 26 from the target image data 18.

Identifying the representation based on image entropy may includedetermining color image entropy curves for the target image data 18. Thetarget image data 18 may be represented in a three-dimensional L*a*b*space with the color entropy curves based on Shannon entropy of each ofthe a*b* planes, of each of the L*a* planes, of each of the L*b* planes,or combinations thereof. The determination of the color entropy curvesmay include dividing the three-dimensional L*a*b* space of the targetimage data 18 into a plurality of cubic subspaces, tabulating the cubicspaces having similar characteristics to arrive at a total cubic spacecount for each characteristic, generating empty image entropy arrays foreach of the dimensions of the three-dimensional L*a*b* space, andpopulating the empty image entropy arrays with the total cubic spacecounts corresponding to each of the dimensions.

Identifying the representation based on image entropy may also includedetermining color difference image entropy curves for the target imagedata 18. The target image data 18 may be represented in athree-dimensional L*a*b* space with the three-dimensional L*a*b* spaceanalyzed in relation to an alternative three-dimensional L*a*b* space.The determination of the color difference entropy curves may includecalculating dL* image entropy, dC* image entropy, and dh* image entropybetween the three-dimensional L*a*b* space and the alternativethree-dimensional L*a*b* space.

Identifying the representation based on image entropy may also includedetermining black and white intensity image entropy from the L* plane ofthe three-dimensional L*a*b* space of the target image data 18.Identifying the representation based on image entropy may also includedetermining average L*a*b* values of the target image data 18.Identifying the representation based on image entropy may also includedetermining L*a*b* values for the center of the most populated cubicsubspace.

The one or more data processors 24 are also configured to execute theinstructions described above to apply the target image data 18 to theone or more feature extraction analysis processes 28′. The one or moredata processors 24 are further configured to execute the instructionsdescribed above to extract the target image features 26 from the targetimage data 18 utilizing the one or more feature extraction analysisprocesses 28′.

In an embodiment, the system 10 is configured to extract the targetimage features 26 from the target image data 18 by identifying therepresentation based on image entropy of the target image features 26.Identifying the representation based on image entropy may includedetermining color image entropy curves for the target image data 18,determining color image entropy curves for the target image data 18,determining black and white intensity image entropy from the L* plane ofthe three-dimensional L*a*b* space of the target image data 18,determining average L*a*b* values of the target image data 18,determining L*a*b* values for the center of the most populated cubicsubspace, or combinations thereof.

With reference to FIGS. 4A and 5A and continuing to reference to FIG. 2, in embodiments, the system 10 further includes a sample database 30.The sample database 30 may be associated with the electronic imagingdevice 16 or separate from the electronic imaging device 16, such as ina server-based or in a cloud computing environment. It is to beappreciated that the one or more data processors 24 are configured to becommunicatively coupled with the sample database 30. The sample database30 may include a plurality of sample images 32, such as a first sampleimage 34 as shown in FIG. 4A and a second sample image 36 as shown inFIG. 5A. In embodiments, each of the plurality of sample images 32 is animage of a panel including a sample coating. A variety of samplecoatings, defining a set of coating formulas, may be imaged to generatethe plurality of sample images 32. The sample images 32 may be imagedutilizing one or more different electronic imaging devices 16 to accountfor variations in imaging abilities and performance of each of theelectronic imaging devices 16. The plurality of sample images 32 may bein any format, such as RAW, JPEG, TIFF, BMP, GIF, PNG, and the like.

The one or more data processors 24 may be configured to execute theinstructions to receive, by the one or more data processors 24, sampleimage data 38 of the sample images 32. The sample image data 38 may begenerated by the electronic imaging device 16. The sample image data 38may define RGB values, L*a*b* values, or a combination thereof,representative of the sample images 32. In certain embodiments, thesample image data 38 defines the RGB values representative of the sampleimages 32, such as shown in FIG. 4B for the first sample image 34 andFIG. 5B for the second sample image 36. The one or more data processors24 may be further configured to execute the instructions to transformthe RGB values of the sample image data 38 to L*a*b* valuesrepresentative of the sample images 32. The system 10 may be configuredto normalize the sample image data 38 of the plurality of sample images32 for various electronic imaging devices 16 thereby improvingperformance of the system 10.

The sample image data 38 may include sample image features 40. Thesample image features 40 may include color and appearancecharacteristics of the sample image 32, representations of the sampleimage data 38, or a combination thereof. In certain embodiments, thesample image features 40 may include representations based on imageentropy.

The one or more data processors 24 are configured to execute theinstructions to retrieve, by the one or more data processors 24, one ormore feature extraction analysis processes 28″ that extract the sampleimage features 40 from the sample image data 38. In embodiments, the oneor more feature extraction analysis processes 28″ are configured toidentify the representation based on image entropy for extracting thesample image features 40 from the sample image data 38. To this end, theone or more data processors 24 may be configured to execute theinstructions to identify the representation based on image entropy forextracting the sample image features 40 from the sample image data 38.It is to be appreciated that the one or more feature extraction analysisprocesses 28″ utilized to extract the sample image features 40 may bethe same as or different than the one or more feature extractionanalysis processes 28′ utilized to extract the target image features 26.

In an embodiment, the system 10 is configured to extract the sampleimage features 40 from the sample image data 38 by identifying therepresentation based on image entropy of the sample image features 40.Identifying the representation based on image entropy may includedetermining color image entropy curves for the sample image data 38,determining color image entropy curves for the sample image data 38,determining black and white intensity image entropy from the L* plane ofthe three-dimensional L*a*b* space of the sample image data 38,determining average L*a*b* values of the sample image data 38,determining L*a*b* values for the center of the most populated cubicsubspace, or combinations thereof.

The one or more data processors 24 are configured to execute theinstructions to retrieve, by one or more data processors, amachine-learning model 42 that identifies a calculated match sampleimage 44 from the plurality of sample images 32 utilizing the targetimage features 26. The machine-learning model 42 may utilize supervisedtraining, unsupervised training, or a combination thereof. In anembodiment, the machine-learning model 42 utilizes supervised training.Examples of suitable machine-learning models include, but are notlimited to, linear regression, decision tree, k-means clustering,principal component analysis (PCA), random decision forest, neuralnetwork, or any other type of machine learning algorithm known in theart. In an embodiment, the machine-learning model is based on a randomdecision forest algorithm.

The machine-learning model 42 includes pre-specified matching criteria46 representing the plurality of sample images 32 for identifying thecalculated match sample image 44 from the plurality of sample images 32.In embodiments, the pre-specified matching criteria 46 are arranged inone or more decision trees. The one or more data processors 24 areconfigured to apply the target image features 26 to the machine-learningmodel 42. In an embodiment, the pre-specified matching criteria 46 areincluded in one or more decision trees with the decisions treesincluding root nodes, intermediate nodes through various levels, and endnodes. The target image features 26 may be processed through the nodesto one or more of the end nodes with each of the end nodes representingone of the plurality of sample images 32.

The one or more data processors 24 are also configured to identify thecalculated match sample image 44 based upon substantially satisfying oneor more of the pre-specified matching criteria 46. In embodiments, thephase “substantially satisfying” means that the calculated match sampleimage 44 is identified from the plurality of sample images 32 by havingthe greatest probability for matching the target coating 12. In anembodiment, the machine-learning model 42 is based on a random decisionforest algorithm including a plurality of decision trees with outcomesof each of the decisions trees, through processing of the target imagefeatures 26, being utilized to determine a probably of each of thesample images 32 matching the target coating 12. The sample image 32having the greatest probability for matching the target coating 12 maybe defined as the calculated match sample image 44.

In embodiments, the one or more data processors 24 are configured toexecute the instructions to generate the pre-specified matching criteria46 of the machine-learning model 42 based on the sample image features40. In certain embodiments, the pre-specified matching criteria 46 aregenerated based on the sample image features 40 extracted from theplurality of sample images 32. The one or more data processors 24 may beconfigured to execute the instructions to train the machine-learningmodel 42 based on the plurality of sample images 32 by generating thepre-specified matching criteria 46 based on the sample image features40. The machine-learning model 42 may be trained at regular intervals(e.g., monthly) based on the plurality of sample images 32 includedwithin the sample database 30. As described above, the sample image data38 defining the RGB values representative of the sample images 32 may betransformed to L*a*b* values with the sample image features 40 extractedfrom the sample image data 38 including L*a*b* values by identifying therepresentations based on image entropy.

The calculated match sample image 44 is utilized for matching color andappearance of the target coating 12. The calculated match sample image44 may correspond to a coating formula potentially matching color andappearance of the target coating 12. The system 10 may include one ormore alternate match sample images 48 related to the calculated matchsample image 44. The one or more alternate match sample images 48 mayrelate to the calculated match sample image 44 based on coating formula,observed similarity, calculated similarity, or combinations thereof. Incertain embodiments, the one or more alternate match sample images 48are related to the calculated match sample image 44 based on the coatingformula. In embodiments, the calculated match sample image 44corresponds to a primary coating formula and the one or more alternatematch sample images 48 correspond to alternate coating formulas relatedto the primary coating formula. The system 10 may include a visual matchsample image 50, selectable by a user, from the calculated match sampleimage 44 and the one or more alternate match sample images 48 based onan observed similarity to the target coating 12 by the user.

With reference to FIGS. 6 and 7 , in embodiments, the electronic imagingdevice 16 further includes a display 52 configured to display thecalculated match sample image 44. In certain embodiments, the display 52is further configured to display a target image 58 of the target coating12 adjacent the calculated match sample image 44. In an embodiment, thedisplay 52 is further configured to display the one or more alternatematch sample images 48 related to the calculated match sample image 44.In embodiments of the electronic imaging device 16 including the camera20, the display 52 may be located opposite of the camera 20.

In embodiments, the system 10 further includes a user input module 54configured to select, by a user, the visual match sample image 50 fromthe calculated match sample image 44 and the one or more alternate matchsample images 48 based on an observed similarity to the target coating12 by the user. In embodiments of the electronic imaging device 16including the display 52, the user may select the visual match sampleimage 50 by touch input on the display 52.

In embodiments, the system 10 further includes a light source 56configured to illuminate the target coating 12. In embodiments of theelectronic imaging device 16 including the camera 20, the electronicimaging device 16 may include the light source 56 and the light source56 may be located adjacent the camera 20.

In embodiments, the system 10 further includes a dark box (not shown)for isolating the target coating 12 to be imaged from extraneous light,shadows, and reflections. The dark box may be configured to receive theelectronic imaging device 16 and permit exposure of target coating 12 tothe camera 20 and the light source 56. The dark box may include a lightdiffuser (not shown) configured to cooperate with the light source 56for sufficiently diffusing the light generated from the light source 56.

A method 1100 for matching color and appearance of the target coating 12is also provided herein with reference to FIG. 8 and continuingreference to FIGS. 1-7 . The method 1100 includes the step 1102 ofreceiving, by one or more data processors, the target image data 18 ofthe target coating 12. The target image data 18 is generated by theelectronic imaging device 16 and includes the target image features 26.The method 1100 further includes the step 1104 of retrieving, by one ormore processors, one or more feature extraction analysis processes 28′that extracts the target image features 26 from the target image data18. The method 1100 further includes the step 1106 of applying thetarget image features 26 to the one or more feature extraction analysisprocesses 28′. The method 1100 further includes the step 1108 ofextracting the target image features 26 from the target image data 18utilizing the one or more feature extraction analysis processes 28′.

The method 1100 further includes the step 1110 of retrieving, by one ormore data processors, the machine-learning model 42 that identifies thecalculated match sample image 44 from the plurality of sample images 32utilizing the target image features 26. The machine-learning model 42includes the pre-specified matching criteria 46 representing theplurality of sample images 32 for identifying the calculated matchsample image 44 from the plurality of sample image 32. The method 1100further includes the step 1112 of applying the target image features 26to the machine-learning model 42. The method 1100 further includes thestep 1114 of identifying the calculated match sample image 44 based uponsubstantially satisfying one or more of the pre-specified matchingcriteria 46.

In embodiments, the method 1100 further includes the step 1116 ofdisplaying, on the display 52, the calculated match sample image 44, theone or more alternate match sample images 48 related to the calculatedmatch sample image 44, and a target image 58 of the target coating 12adjacent the calculated match sample image 44 and the one or morealternate match sample images 48. In embodiments, the method 1100further includes the step 1118 of selecting, by the user, the visualmatch sample image 50 from the calculated match sample image 44 and theone or more alternate match sample images 48 based on the observedsimilarity to the target image data 18.

With reference to FIG. 9 and continuing reference to FIGS. 1-8 , inembodiments, the method 1100 further includes the step 1120 ofgenerating the machine-learning model 42 based on the plurality ofsample images 32. The step 1120 of generating the machine-learning model42 may include the step 1122 of retrieving the plurality of sampleimages 32 from the sample database 30. The step 1120 of generating themachine-learning model 42 may further include the step 1124 ofextracting the sample image features 40 from the plurality of sampleimages 32 based on one or more feature extraction analysis processes28′. The step 1120 of generating the machine-learning model 42 mayfurther include the step 1126 of generating the pre-specified matchingcriteria 46 based on the sample image features 40.

With reference to FIG. 10 and continuing reference to FIGS. 1-9 , inembodiments, the method 1100 further includes the step 1128 of forming acoating composition corresponding to the calculated match sample image44. The method 1100 may further include the step 1130 of applying thecoating composition to the substrate 14.

The method 1100 and the system 10 disclosed herein can be used for anycoated article or substrate 14, including the target coating 12. Someexamples of such coated articles can include, but not limited to, homeappliances, such as refrigerator, washing machine, dishwasher, microwaveovens, cooking and baking ovens; electronic appliances, such astelevision sets, computers, electronic game sets, audio and videoequipment; recreational equipment, such as bicycles, ski equipment,all-terrain vehicles; and home or office furniture, such as tables, filecabinets; water vessels or crafts, such as boats, yachts, or personalwatercrafts (PWCs); aircrafts; buildings; structures, such as bridges;industrial equipment, such as cranes, heavy duty trucks, or earthmovers; or ornamental articles.

Color matching for effect pigment-based coatings is particularlychallenging. Effect coatings include metallic coatings and pearlescentcoatings, but may also include other effects such as phosphorescence,fluorescent, etc. Metallic and pearlescent coatings include an effectadditive 74, as illustrated in FIG. 11 with continuing reference toFIGS. 1-10 . A coating, including a target coating 12 and/or a samplecoating 60 as illustrated, may include several layers overlying thesubstrate 14. The target coating 12 described above may include the samelayers as the sample coating 60 illustrated in FIG. 11 , so thedescription of the layers of the sample coating 60 also applies to thetarget coating 12 described above. As used herein, the term “overlying”means “over” such that an intervening layer may lie between theoverlying component (the sample coating 60 in this example) and theunderlying component (the substrate 14 in this example,) or “on” suchthat the overlying component physically contacts the underlyingcomponent. Moreover, the term “overlying” means a vertical line passingthrough the overlying component also passes through the underlyingcomponent, such that at least a portion of the overlying component isdirectly over at least a portion of the underlying component. It isunderstood that the substrate 14 may be moved such that the relative“up” and “down” positions change. Spatially relative terms, such as“top”, “bottom”, “over” and “under” are made in the context of theorientation of the cross-sectional FIG. 11 . It is to be understood thatspatially relative terms refer to the orientation in FIG. 11 , so if thesubstrate 14 were to be oriented in another manner the spatiallyrelative terms would still refer to the orientation depicted in FIG. 11. Thus, the terms “over” and “under” remain the same even if thesubstrate 14 is twisted, flipped, or otherwise oriented other than asdepicted in the figures.

FIG. 11 illustrates a primer 62 overlying the substrate 14, and a basecoat 64 overlying the primer 62. The primer 62 and substrate 14 are notconsidered part of the sample coating 60 in this description. Anoptional effect coat 66 overlies the base coat 64, and a clear coat 68overlies the optional effect coat 66. A sample coating formula 70includes a plurality of components 72, where the components 72 for thebase coat 64 may not be the same as the components 72 for the optionaleffect coat 66 and/or the clear coat 68. One or both of the base coat 64and the effect coat 66 include an effect additive 74 as one of thecomponents 72. The effect additive 74 is utilized for producing thespecial effect of the sample coating 60, such as producing a metallizedor pearlescent effect. The sample coating formula 70 includes a basecoat formula 65 for the base coat 64, an optional effect coat formula 67for the optional effect coat 66, and a clear coat formula 69 for theclear coat 68. The sample coating formula 70 may include formulas forother optional layers as well in some embodiments.

A metallic effect is produced when the sample coating 60 (or any othercoating) includes reflective flakes that are visible. The reflectiveflakes serve as the effect additive 74. In an embodiment, metalparticles in the paint pick up and reflect more incident light than thebasic paint colors, giving a coating a varied appearance over a givenarea. Some of the coating will appear as the color of the base coat, andother portions will reflect light and appear as a sparkle or glimmer.The metallic color is a color that appears to be that of a polishedmetal. The visual sensation usually associated with metals is a metallicshine that is different than a simple solid color. The metallic colorincludes a shiny effect that is due to the material's brightness, andthat brightness varies with the surface angle relative to a lightsource. One technique utilized to produce a metallic effect color is toadd aluminum flakes (which are an example of an effect additive 74) to apigmented coating. The aluminum flakes produce a sparkle that varies insize, brightness, and sometimes in color depending on how the flakes areprocessed. Larger flakes produce a coarser sparkle, and smaller flakesproduce a finer sparkle. Different types of flakes may be utilized, suchas “silver dollar” flakes that are flat and relatively circular, like asilver dollar coin. Other types of flakes may have jagged edges, like acorn flake. The flakes may also be colored in some embodiments, so theflake produces a colored sparkle.

Adding the aluminum flakes to the base coat 64 produces a metalliceffect, but if the same type and amount of flakes are added to atranslucent effect coat 66 that overlies the base coat 64, the coatinghas a different appearance that is “deeper.” In another embodiment, thebase coat 64 may include one type and amount of effect additive 74, andthe effect coat 66 may include a different type and/or amount of effectadditive 74 to produce yet another appearance. Therefore, many variablescan influence the appearance of the metallic color, such as the type offlake, the size of the flake, the coat that includes the flake, the basecolor, etc. Therefore, it is difficult to match a metallic effectbecause of the wide variety of factors and appearances that arepossible.

A pearlescent coating includes an effect additive 74 that selectivelyreflects, absorbs, and/or transmits visible light, which can result in acolorful appearance that varies based on the flake's structure andmorphology. This gives the coating a sparkle as well as a deep colorthat changes with viewing angle and/or lighting angle. The effectadditive 74 in a pearlescent coating may be ceramic crystals, and theseeffect additives 74 may be added to the base coat 64, the effect coat66, or both. Furthermore, the pearlescent effect additive 74 may be ofvarying grades with different sizes, refractive indices, shapes, etc.,and the different grades may be used alone or in combination. It is alsopossible to combine a pearlescent effect additive 74 with a metalliceffect additive 74, in a wide variety of combinations, and theappearance of the coating will vary with changes in the effect additiveconcentration, type, positioning, etc. All the different possiblevariations in the effect additive 74 may apply to a single color, so atechnique that matches just the color will not be effective atreproducing the appearance of the effect pigment-based coating.

As mentioned above, the effect pigment-based coating has a varyingappearance, so different pixels within an image will have differentcolors, brightness, hue, chroma, or other appearance features, as seenin the illustrations in FIGS. 3A, 4A, and 5A. Because of this, a colormatching protocol that breaks the image into pixels, and then analyzesthe image pixel by pixel to determine a pixel difference between two ormore pixels, can aid in matching the overall appearance of an effectpigment-based coating. A mathematical model, such as the machinelearning model described above, can be utilized to determine features ofan image based on differences between pixels within an image. As such, atarget image 58 can be analyzed pixel by pixel with a mathematical modelto produce a target image feature 26, as mentioned above, and this maybe combined with other target image features 26 such as color (which maybe determined for the target image 58 as a whole instead of a pixel bypixel determination) or other target image features. The resulting oneor more target image features 26 may be compared to a sample database 30that has produced similar sample image features 40 to find the bestmatch, again as described above. The pixel-by-pixel evaluation canproduce a match for effect pigments that is not possible withevaluations based on a target image 58 as a whole.

Reference is made to an embodiment illustrated in FIG. 12 , withcontinuing reference to FIGS. 1-11 . An imaging device 16 captures atarget image 58 of a target coating 12, as described above. The imagingdevice 16 is set at an imaging angle 76 relative to a surface of thetarget coating 12, and an illumination source 78 is set at anillumination angle 80 during capture of the target image 58. The targetimage 58 is divided into a plurality of target pixels 82, where theplurality of target pixels 82 vary such that at least one target pixel82 is different than another target pixel 82 in appearance.

In a similar manner, with reference to an embodiment illustrated in FIG.13 with continuing reference to FIGS. 1-12 , the image device 16captures a sample image 32 of a sample coating 60, again as describedabove. The image device 16 used to capture the sample image 32 may bethe same as the image device 16 used to capture the target image 58, butdifferent imaging devices 16 may also be utilized. An illuminationsource 78 is also utilized for capturing the sample image 32, where theimaging device is set at the imaging angle 76 relative to a surface ofthe sample coating 60 and the illumination source 78 is set at theillumination angle 80, similar to as described for the target image 58in FIG. 12 . The sample image 32 is divided into a plurality of samplepixels 84, where one sample pixel 84 has a different appearance thananother sample pixel 84. In an embodiment, the sample coating 60includes an effect additive 74, which is illustrated in the illustrationof the sample image 32 with small dots, and which produces variations inthe appearance of the sample pixels 84 within the sample image 32. Thetarget and sample coatings 12, 60 in FIGS. 12 and 13 , respectively, mayinclude a base coat 64, an optional effect coat 66, and a clear coat 68,but variations in the coats that are present are also possible.

A sample database 30 is produced for matching a target image 58 with asample coating formula 70, as illustrated in an embodiment in FIG. 14with continuing reference to FIGS. 1-13 . Production of the sampledatabase 30 includes producing a sample coating formula 70, where thesample coating formula 70 includes an effect additive 74. The samplecoating formula 70 may include a base coat 64 and a clear coat 68, or abase coat 64, an effect coat 66, and a clear coat 68, but otherembodiments are also possible. For example, any one of the base coat 64,optional effect coat 66, and clear coat 68 may include a plurality oflayers, and other coating layers may also be present.

Once the sample coating formula 70 is produced, a sample coating 60 isproduced with the sample coating formula 70. This is typically done byapplying the material from the sample coating formula 70 onto asubstrate 14, such as by spray painting, applying with a brush, dipcoating, digital printing, or any other coating technique. In anembodiment, the sample coating 60 is formed by spray painting, where thespray painting utilizes recommended spray painting conditions for thegrade of coating in the sample coating formula 70. The spray paintingconditions may include the type of solvent, the quantity of solvent, thespray gun pressure, the type and/or size of spray gun nozzle used, thedistance between the spray gun and the substrate 14, etc. Producing thesample coating 60 using the same technique typically utilized by autobody repair shops or others that may need to match a target coating 12may provide a more accurate representation of the finished product thatcan be expected than if the sample coating 60 were applied with anothertechnique. The sample coating formula 70 included one or more effectadditives 74, so the sample coating 60 has a varied appearance where oneportion of the sample coating 60 appears different than another portion.For example, a sparkle appears different than a matte color.

A sample image 32 is then produced with an imaging device 16, such as byphotographing the sample coating 60. The sample image 32 includes aplurality of sample image data 38, such as RGB values, L*a*b* values,etc. The sample image 32 may be one or more still images, or a movingimage. In an embodiment, the sample image 32 includes a plurality ofstill images captured with known and specified illumination, imagingangles, distance between the imaging device 16 and the sample coating60, and illumination angles. The sample image 32 may also be directed toan essentially flat portion of the sample coating 60, but in someembodiments the sample image may also include one or more images of asample coating 60 with a known curvature.

A feature extraction analysis process 28″ may be applied to the sampleimage data 38 to produce a sample image feature 40. The sample imagefeature 40 and the sample coating formula 70 are then linked togetherand saved in the sample database 30. The sample database 30 may includea plurality of sample image features 40 linked to one sample coatingformula 70, and one or more sample images 32 may also be linked with thesample coating formula 70. The sample database 30 is saved in one ormore storage devices 22, which may be the same or different than thestorage device(s) 22 mentioned above for matching the target coating 12.The storage device 22 that saves the sample database 30 may beassociated with a data processor 24, as described above, to executeinstructions for saving and retrieving data from the sample database 30.The process illustrated in FIG. 14 may be repeated for a wide variety ofsample coating formulas 70, so a plurality of sample coating formulas 70are saved in the sample database 30. The sample coating formulas 70 mayalso be configured to about match original equipment coatings providedby vehicle manufacturers, with the intent to match vehicle coatings witha sample coating formula 70. One or more of the sample image(s) 32 maybe utilized as the calculated match sample image 44, or a different setof parameters may be utilized to capture the calculated match sampleimage 44 from the sample coating 60.

A plurality of sample coating formulas 70 that produce about the samesample image 32 may be produced, where the sample coating formulas 70are different from each other and include different grades of a coating.Some vehicle repair shops tend to utilize one grade of coating, and thegrade of coating may vary from one vehicle repair shop to the next. Asample database 30 that includes a matching sample coating formula 70that utilizes the same grade of coating that the vehicle repair shop isfamiliar with may improve the results because of the skill andfamiliarity the vehicle repair shop has with a particular grade ofcoating. In this manner, the same sample database 30 can be utilized bydifferent vehicle repair shops (or other users of the sample database30) that typically use different grades of a coating.

The feature extraction analysis process 28″ described above for FIG. 14is shown in greater detail in an embodiment in FIG. 15 , with continuingreference to FIGS. 1-14 . The sample image 32 is divided into aplurality of sample pixels 84. Each sample pixel 84 has sample pixelimage data, and the sample pixel image data is utilized to determine asample pixel feature 86 for the sample pixel 84. The sample pixelfeature 86 may be different than an overall sample feature 40, becausethe sample pixel 84 may have a different appearance than the overallsample image 32. The sample pixel feature 86 may be a determination ofthe RGB values for that sample pixel 84, or the L*a*b* values for thatsample pixel 84, or other appearance attributes for that sample pixel84. In an embodiment, a sample pixel feature 86 is determined for all ofthe sample pixels 84 of the sample image 32, but a sample pixel feature86 may be determined for only a subset of the sample pixels 84 of thesample image 32 in alternate embodiments. In all embodiments, a samplepixel feature 86 is determined for a plurality of the sample pixels 84,where that sample pixel feature 86 varies for at least some of thesample pixels 84. A sample pixel feature difference 88 is thendetermined for the sample pixels 84. The sample image feature 40 is thendetermined from the sample pixel feature difference 88.

An embodiment is illustrated in FIG. 16 , with continuing reference toFIGS. 1-15 , where a three-dimensional coordinate system 90 is utilized.The three-dimensional coordinate system 90 may represent the RGB colorsystem, where one axis of the three-dimensional coordinate system 90 isthe R value, another axis is the G value, and the third axis is the Bvalue. Alternatively, the three-dimensional coordinate system 90 mayrepresent the L*a*b* color system, where one axis is the L* value,another axis is the a* value, and the third axis is the b* value. Thethree-dimensional coordinate system 90 may represent other axis inalternate embodiments, and in some embodiments a three-dimensionalcoordinate system 90 may not be utilized. The sample pixel features 86may then be plotted in the three-dimensional coordinate system 90, andthe number of sample pixels 84 that fall within each block of thethree-dimensional coordinate system 90 may be tallied. In thehypothetical illustration in FIG. 16 , the first block nearest the 0-0-0coordinate has a tally of 1, the block directly above it has a tally of2, and the block directly above that has a tally of 5. Alternatetechniques may be utilized to determine the sample pixel featuredifference 88, and a plurality of techniques may be utilized todetermine a plurality of sample pixel feature differences 88 for onesample image 32 in a variety of manners.

A sample image feature 40 may then be determined from the sample pixelfeature differences 88, as illustrated in FIG. 15 with continuingreference to FIGS. 1-14 and FIG. 16 . Determining a sample image feature40 from a plurality of sample pixel feature differences 88 is referredto herein as a spatial micro-color analysis, because the sample imagefeature 40 is based on color or appearance changes in difference spaceswithin a sample image 32. There may be plurality of sample imagefeatures 40 determined for one single sample image 32, and some of thosesample image features 40 may be based on spatial micro-color analysis,and others may not. However, in this description, at least one of thesample image features 40 is based on spatial micro-color analysis. Assuch, the sample database 30 includes at least one sample image feature40 that is based on spatial micro-color analysis. The sample image data38 block at the top of FIG. 15 and the sample image feature 40 block atthe bottom of FIG. 15 show an embodiment of the steps utilized betweenthe comparably labeled blocks in FIG. 14 . A calculated match sampleimage 44 may also be saved in the sample database 30, where thecalculated match sample image 44 may be utilized for display to a useras described above. The user can then determine whether the calculatedmatch sample image 44 is an acceptable match for the target coating 12based on a visual analysis. The calculated match sample image 44 may becaptured with the imaging device 16 under conditions that render thecalculated match sample image 44 acceptable for visual analysis.

Reference is made to an embodiment illustrated in FIG. 17 , withcontinuing reference to FIGS. 1-16 . FIG. 17 illustrates an embodimentfor utilizing spatial micro-color analysis on the target image 58, asexemplified in the feature extraction analysis process 28′ block in FIG.2 . As such, the target image data 18 block at the top of FIG. 17 andthe target image feature 26 block third from the bottom of FIG. 16 aretaken from the comparably named blocks in FIG. 2 . FIG. 17 alsoillustrates an embodiment where the mathematical model 100 utilized tocompare the target image feature 26 with the sample image feature 40 issomething other than a machine learning model 42. However, it is to beunderstood that a machine learning model 42 may still be utilized insome embodiments. The sample image features 40 illustrated in FIGS. 2and 17 are derived from the sample database 30, as described above.

The target image 58 is divided into a plurality of target pixels 82, asillustrated in FIG. 12 and FIG. 17 . Each target pixel 82 includes atarget pixel image data, where the target pixel image data is at least apart of the target image data 18. A target pixel feature 102 isdetermined from the target pixels 82, where each of the target pixels 82has at least some of the target image data 18 and at least some of thattarget image data 18 for the different target pixels 82 varies, asdescribed above for the sample pixel features 86. A target pixel featuredifference 104 is then determined from the target pixel features 102,and a target image feature 26 is then determined from the target pixelfeature differences 104, again as described above for the sample pixelfeature 86. As such, because at least some of the target pixels 82 havedifferent target image data 18, at least one of the target imagefeatures 26 is determined by spatial micro-color analysis. The targetimage features 26 for a single target image 58 may include one or moretarget image features 26 that are determined by spatial micro-coloranalysis, and may also include one or more target image features 26 thatare not based on spatial micro-color analysis. Pre-specified matchingcriteria 46 are then utilized to determine a calculated match sampleimage 44, as described above.

A wide variety of spatial micro-color analysis mathematical techniquesmay be utilized to determine the target image feature 26 and/or thesample image feature 40, and the same techniques may be utilized in someembodiments to facilitate matching. A partial listing of spatialmicro-color analysis mathematical techniques is listed below, where thegeneric terms pixel means either a target or sample pixel 82, 84; andimage means either a target or sample image 58, 32. Examples of spatialmicro-color analysis mathematical techniques include, but are notlimited to: determining L*a*b* color coordinates of individual pixels ofthe image; determining average L*a*b* color coordinates from individualpixels for a total image of the image; determining sparkle area of ablack and white image of the image; determining sparkle intensity of theblack and white image of the image; determining sparkle grade of theblack and white image of the image; determining sparkle color of theimage; determining sparkle clustering of the image; determining sparklecolor differences within the image; determining sparkle persistence ofthe image, where sparkle persistence is a measure of the sparkle as afunction of one or more illumination changes during capture of theimage; determining color constancy, at a pixel level, with one or moreillumination changes during capture of the image; determining waveletcoefficients of the image at the pixel level; determining Fouriercoefficients of the target image at the target pixel level; determiningaverage color of local areas within the image, where the local area maybe one or more pixels, but where the local area is less than a totalarea of the image; determining pixel count in discrete L*a*b* ranges ofthe image, where the L*a*b* range may be fixed or may be data drivensuch that the range varies; determining maximally populated coordinatesof cubic bins at the pixel level of the image, where the cubic bins arebased on a 3 dimensional coordinate mapping using L*a*b* or RGB values;determining overall image color entropy of the image; determining imageentropy of one or more of the L*a*b* planes as a function of the 3^(rd)dimension of the image; determining image entropy of one or more of theRGB planes as a function of the 3^(rd) dimension of the image;determining local pixel variation metrics of the image; determiningcoarseness of the image; determining vectors of high variance of theimage, where the vectors of high variance are established usingprinciple component analysis; and determining vectors of high kurtosisof the image, where the vectors of high kurtosis are established usingindependent component analysis.

Determining L*a*b* color coordinates of individual pixels of thetarget/sample image includes breaking the image into pixels, and thendetermining the L*a*b* color coordinates for a plurality (or all in someembodiments) of the pixels. Determining average L*a*b* color coordinatesfrom individual pixels for a total image of the image means averagingthe L*a*b* samples for each of the pixels within the image. Determiningsparkle area of a black and white image of the image means rendering orobtaining the image in black and white, and then determining how manypixels include a sparkle and a total number of the pixels within a givenarea. The given area may be the entire image, or a subset of the image.The sparkle area is then determined by dividing the number of pixels inthe given area that include a sparkle by the total number of the pixelsin that given area. Determining sparkle intensity of the black and whiteimage of the image means determining the average brightness of thepixels that include a sparkle within a given area.

Determining sparkle grade of the black and white image means determininga value derived from the sparkle area and sparkle intensity thatdescribes the visual perception of the sparkle phenomena. Determiningsparkle color of the image means determining the location of a sparkle,and then determining the color of that sparkle. Determining sparkleclustering of the image means determining the various colors of sparklethat are present in an image using clustering or distribution fittingalgorithms. Determining sparkle color differences within the image meansdetermining the color of the sparkles in the image, as mentioned above,and then determining the variation or differences in that color in thesparkles. Determining sparkle persistence of the image means determiningif a sparkle remains within a given pixel (and if the sparkle has achange in brightness) when the illumination changes during capture ofthe image, and where the imaging angle 76 and the illumination angle 80remain the same. Determining color constancy, at a pixel level, meansdetermining if a color of a pixel remains the same within a given pixelwhen the illumination changes during capture of the image, and where theimaging angle 76 and the illumination angle 80 remain the same. At leasttwo different images are needed to determine sparkle persistence andcolor constancy.

Determining the Fourier coefficients of the image at the pixel levelmeans determining coefficients associated with the image afterdecomposing the image into sinusoid components of different frequencyusing the Fourier transform, where the coefficients describe thefrequencies present in the image. This can be determined for a givennumber of pixels within a given area of the image. Determining waveletcoefficients of the image at the pixel level means determining thecoefficients associated with the image after decomposing the image intocomponents associated with shifted and scaled versions of a waveletusing the discrete or continuous wavelet transform, where thecoefficients describe the image content associated with the relevantlyshifted and scaled version of the wavelet. A wavelet is a function thattends towards zero at the extrema with a local area containing anoscillation. This can be determined for a given number of pixels withina given area of the image. Determining average color of local areaswithin the image means determining the average color of one or morepixels within a sample area that is less than the total area of theimage. Determining the pixel count in discrete L*a*b* ranges of theimage means determining the pixel count within a given area of athree-dimensional coordinate system 90 using the L*a*b* values as theaxes, similar to the illustration in FIG. 16 . The size of the givenarea within the three-dimensional coordinate system 90 may be fixed, orthe size may vary depending on the count values generated. Determiningmaximally populated coordinates of cubic bins at the pixel level of theimage, where the cubic bins are based on a three-dimensional coordinatesystem 90 using L*a*b* or RGB values as the axes, means determining theblocks with the highest pixel counts as illustrated in FIG. 16 . Setvalues, percentage values, or other metrics may be used to determine thevalue that designates a block as being “maximally” populated.

Determining overall image color entropy of the image means determiningthe overall color entropy of the image based on variations within thepixels. Color entropy may be Shannon entropy or other types of entropycalculations. Determining image entropy of one or more of the L*a*b*planes or RGB planes as a function of the 3^(rd) dimension of the imagemeans selecting a plane within the three-dimensional coordinate system90 as illustrated in FIG. 16 , and then determining the entropy based onthe values along that plane. The entropy can be Shannon entropy or othertypes of entropy calculations, as mentioned above. Determining localpixel variation metrics of the image means selecting essentially anypixel feature and then determining the variation of that pixel featurewithin the image. Determining coarseness of the image means determiningthe impression of coarseness of an image based on shadowing or otherfeatures that suggest coarseness. Determining vectors of high varianceof the image means determining vectors based on a vector origin at acoordinate origin, where the vectors of high variance are establishedusing principle component analysis. The vectors of high variance can beconsidered target or sample image features 26, 40, or pixel data fromthe image can be projected onto the vectors to derive new target orsample image features 26, 40. Determining vectors of high kurtosis ofthe image again means determining vectors based on a vector origin atthe coordinate origin, where the vectors of high kurtosis areestablished using independent component analysis. The vectors of highkurtosis can be considered target or sample image features 26, 40, orpixel data from the image can be projected onto the vectors to derivenew target or sample image features 26, 40.

A method of matching a target coating 12 is provided in anotherembodiment, as illustrated in FIG. 18 with continuing reference to FIGS.1-17 . The method includes obtaining 1200 a target image 58 of a targetcoating 12, where the target coating 12 is an effect pigment-basedcoating that includes an effect additive 74. The method further includesapplying 1210 a feature extraction analysis process 28′ to the targetimage 58, where the feature extraction analysis process 28′ includesdividing 1220 the target image 58 into a plurality of target pixels 82that include target pixel image data; determining 1230 a target pixelfeature 102 for the individual target pixels of the plurality of targetpixels 82; determining 1240 a target pixel feature difference 104between the individual target pixels 82; determining 1250 a target imagefeature 26 from the target pixel feature difference 104; and calculating1260 the calculated match sample image 44 with the target image feature26 based upon substantially satisfying one or more pre-specifiedmatching criteria.

A method of producing a sample database 30 is also provided, asillustrated in FIG. 19 with continuing reference to FIGS. 1-18 . Themethod includes 1300 preparing a sample coating 60 from a sample coatingformula 70 that includes an effective additive 74 such that theappearance of the sample coating 60 varies from one location to another.The method also includes imaging 1310 the sample coating to produce asample image 32 that comprises sample image data, where the sample image32 is divided into a plurality of sample pixels 84 that each comprisesample pixel image data. The next step is retrieving 1320 one or moresample image features 40 from the sample image data, where at least oneof the sample image features 40 comprises a spatial micro-color analysisthat includes a value determined by a sample pixel feature difference 88between at least two of the sample pixels 84. Another step is saving1330 the sample coating formula 70 and one or more sample image features40 in the sample database 30, where the sample coating formula 70 islinked to the one or more sample image features 40.

Reference is made to FIG. 20 . Once the calculated match sample image 44is obtained, it may be approved by an operator. At that time, a repaircoating 110 may be prepared using the sample coating formula 70 thatcorresponds to the calculated match sample image 44. In an exemplaryembodiment, the sample coating formula 70 includes an effect additive 74and one or more other components 72. The repair coating 110 may beapplied to a substrate 14, such as a vehicle needing repairs, using oneor more of a wide variety of techniques. In an exemplary embodiment, therepair coating 110 is applied to the substrate 12 utilizing digitalprinter 112 and a digital printing technique, as illustrated, but inalternate embodiments the repair coating 110 may be applied to thesubstrate 12 by other techniques including, but not limited to, spraypainting, applying with a brush, and/or dip coating.

While at least one embodiment has been presented in the foregoingdetailed description, it should be appreciated that a vast number ofvariations exist. It should also be appreciated that the embodiment orembodiments are only examples, and are not intended to limit the scope,applicability, or configuration in any way. Rather, the foregoingdetailed description will provide those skilled in the art with aconvenient road map for implementing an embodiment, it being understoodthat various changes may be made in the function and arrangement ofelements described in an embodiment without departing from the scope asset forth in the appended claims and their legal equivalents.

What is claimed is:
 1. A system for matching a target coatingcomprising: a storage device for storing instructions; one or more dataprocessors configured to execute instructions to: receive a target imageof the target coating, wherein the target image comprises target imagedata; apply a feature extraction analysis process to the target imagedata to determine a target image feature comprising extracting a targetpixel feature from the target image data based on image entropy of thetarget pixel feature, wherein the feature extraction analysis processcomprises dividing the target image into a plurality of target pixels,determining the target pixel feature for individual target pixels of theplurality of target pixels, determining a target pixel featuredifference between the individual target pixels, and using the targetpixel feature difference to determine the target image feature; anddetermine a calculated match sample image with the target image featurebased upon substantially satisfying one or more pre-specified matchingcriteria.
 2. The system of claim 1 wherein the feature extractionanalysis comprises a spatial micro-color analysis.
 3. The system ofclaim 1 wherein the one or more data processors are configured to applythe feature extraction analysis process, wherein the feature extractionanalysis process determines one or more of: image entropy of one or moreof the L*a*b* planes as a function of the 3^(rd) dimension of the targetimage; and image entropy of one or more of an RGB plane as a function ofthe 3^(rd) dimension of the target image.
 4. The system of claim 1wherein extracting the target pixel feature further comprisesdetermining color image entropy curves for the target image data.
 5. Thesystem of claim 1, further comprising a dark box to isolate the targetcoating from extraneous light, shadows, and reflection.
 6. The system ofclaim 1 wherein the one or more data processors are configured todetermine a coating formula that corresponds to the calculated matchsample image.
 7. The system of claim 6 wherein the one or more dataprocessors are configured to determine a plurality of the coatingformulas that correspond to the calculated match sample image, whereinthe plurality of coating formulas comprise different grades of coatings.8. The system of claim 1, wherein the target pixel feature is an L*a*b*color coordinate.
 9. The system of claim 1 wherein the one or more dataprocessors are configured to reference a sample database to determinethe calculated match sample image.
 10. The system of claim 1 wherein theone or more data processors are configured to receive the target imagedata of the target coating, wherein the target image data correlates toa plurality of images of the target coating with varying angles of lightrelative to an imaging device.
 11. The system of claim 1 wherein the oneor more data processors are configured to receive the target image dataof the target coating, wherein the target image data correlates to aplurality of images of the target coating with varying magnification.12. The system of claim 1 wherein the target coating is a metalliccoating, a pearlescent coating, or a combination of thereof.
 13. Thesystem of claim 1 further comprising an imaging device, wherein theimaging device is configured to generate the target image data of thetarget coating.
 14. The system of claim 1 wherein the one or more dataprocessors are further configured to retrieve a sample image from asample database; extract a sample image feature from the sample imageutilizing the feature extraction analysis process; and generate the oneor more pre-specified matching criteria based on the sample imagefeature.
 15. The system of claim 1 wherein the one or more dataprocessors are configured to determine the calculated match sample imagewith about the same color as that of the target image.
 16. A method ofmatching a target coating comprising: obtaining, by one or more dataprocessors, a target image of the target coating, wherein the targetcoating is an effect pigment-based coating, and wherein the target imagecomprises target image data; applying, by one or more data processors, afeature extraction analysis process to the target image data, whereinthe feature extraction analysis process comprises: dividing the targetimage into a plurality of target pixels; determining a target pixelfeature for individual target pixels of the plurality of target pixels,wherein determining the target pixel feature comprises extracting thetarget pixel feature from the target image data based on image entropyof the target pixel feature; determining a target pixel featuredifference between the individual target pixels; and determining atarget image feature from the target pixel feature difference; anddetermining a calculated match sample image with the target imagefeature based upon substantially satisfying one or more pre-specifiedmatching criteria.
 17. The method of claim 16 wherein applying thefeature extraction analysis process comprises applying the featureextraction analysis process wherein the feature extraction analysisprocess comprises one or more of: determining L*a*b* color coordinatesof the individual target pixels of the target image; and determiningaverage L*a*b* color coordinates from the individual target pixels for atotal image of the target image.
 18. The method of claim 16 whereindetermining the calculated match sample image comprises comparing asample image feature of a sample image to the target image feature. 19.The method of claim 16 further comprising: determining a sample coatingformula that corresponds to the calculated match sample image, whereinthe sample coating formula comprises an effect additive; preparing arepair coating with the sample coating formula that corresponds to thecalculated match sample image; and applying the repair coating to asubstrate.
 20. The method of claim 19 wherein applying the repaircoating to the substrate comprises applying the repair coating to thesubstrate with a digital printer utilizing a digital printing technique.